Yes! This is perfect. Thank you so much. On Mon, May 13, 2024 at 12:11 Roger Bivand <roger.biv...@nhh.no> wrote:
> Is Tony Smith's "Estimation Bias in Spatial Models with Strongly Connected > Weight Matrices" at https://doi.org/10.1111/j.1538-4632.2009.00758.x > helpful? > > Roger > > -- > Roger Bivand > Emeritus Professor > Norwegian School of Economics > Postboks 3490 Ytre Sandviken, 5045 Bergen, Norway > roger.biv...@nhh.no > > ________________________________________ > From: R-sig-Geo <r-sig-geo-boun...@r-project.org> on behalf of Josiah > Parry <josiah.pa...@gmail.com> > Sent: 13 May 2024 17:12 > To: r-sig-Geo@r-project.org > Subject: [R-sig-Geo] Maximum sparsity for spatial regression > > As I'm reading through Modern Spatial Econometrics in Practice, we assume > the spatial weights matrix to be sparse. At one point they note that the > contiguity matrix for the US counties is 0.18% non-zero. But what % > non-zero is too dense? > > I am wondering if there is any research or papers that document what a > recommended upper bound of sparsity should be for one of these models? Is > 10% non-zero too much or sufficient? I suspect the answer is, like most > things, "it depends." > > But, thinking of a situation where someone might use a distance band to > specify neighbors they might create a bandwidth that can encompass 50% or > more of points if using max(knn=1) to specify the distance. I suspect using > a kernel or IDW could reduce the weights close to zero making the impact > minimal. > > Nonetheless, I'm curious if others have thought about this or written about > it! > > Thanks, > > Josiah > > [[alternative HTML version deleted]] > > _______________________________________________ > R-sig-Geo mailing list > R-sig-Geo@r-project.org > https://stat.ethz.ch/mailman/listinfo/r-sig-geo > [[alternative HTML version deleted]] _______________________________________________ R-sig-Geo mailing list R-sig-Geo@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-geo
